Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations48532
Missing cells56154
Missing cells (%)4.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory61.4 MiB
Average record size in memory1.3 KiB

Variable types

Numeric1
Categorical10
Text12
DateTime1

Alerts

Acceptance Status is highly overall correlated with Inspection to Subsidy Redeemed Days and 3 other fieldsHigh correlation
Inspection to Subsidy Redeemed Days is highly overall correlated with Acceptance Status and 1 other fieldsHigh correlation
Subsidy Redeemed Date is highly overall correlated with Acceptance Status and 2 other fieldsHigh correlation
Subsidy Redeemed to Released Days is highly overall correlated with Acceptance Status and 2 other fieldsHigh correlation
Subsidy Released Date is highly overall correlated with Acceptance Status and 3 other fieldsHigh correlation
Subsidy Redeemed Date is highly imbalanced (83.2%) Imbalance
Subsidy Released Date is highly imbalanced (67.6%) Imbalance
Inspection to Subsidy Redeemed Days is highly imbalanced (95.5%) Imbalance
Subsidy Redeemed to Released Days is highly imbalanced (99.9%) Imbalance
Registration to Approval Days has 8022 (16.5%) missing values Missing
Approval to Vendor Selection Days has 8022 (16.5%) missing values Missing
Vendor Selection to Acceptance Days has 8022 (16.5%) missing values Missing
Acceptance to Installation Days has 8022 (16.5%) missing values Missing
Installation to Inspection Days has 8022 (16.5%) missing values Missing
Inspection to Subsidy Redeemed Days has 8022 (16.5%) missing values Missing
Subsidy Redeemed to Released Days has 8022 (16.5%) missing values Missing
Application Number has unique values Unique

Reproduction

Analysis started2025-03-26 15:51:39.898397
Analysis finished2025-03-26 15:51:44.739492
Duration4.84 seconds
Software versionydata-profiling vv4.15.1
Download configurationconfig.json

Variables

Application Number
Real number (ℝ)

Unique 

Distinct48532
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54923446
Minimum10001297
Maximum99995104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size379.3 KiB
2025-03-26T21:21:44.837214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10001297
5-th percentile14414150
Q132279197
median55030379
Q377521938
95-th percentile95511791
Maximum99995104
Range89993807
Interquartile range (IQR)45242742

Descriptive statistics

Standard deviation26014250
Coefficient of variation (CV)0.47364562
Kurtosis-1.203401
Mean54923446
Median Absolute Deviation (MAD)22608814
Skewness0.0018436336
Sum2.6655447 × 1012
Variance6.7674119 × 1014
MonotonicityNot monotonic
2025-03-26T21:21:44.950993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20235135 1
 
< 0.1%
67744287 1
 
< 0.1%
58136760 1
 
< 0.1%
68329500 1
 
< 0.1%
27915522 1
 
< 0.1%
11444916 1
 
< 0.1%
72414948 1
 
< 0.1%
29574182 1
 
< 0.1%
90662933 1
 
< 0.1%
54610571 1
 
< 0.1%
Other values (48522) 48522
> 99.9%
ValueCountFrequency (%)
10001297 1
< 0.1%
10006983 1
< 0.1%
10010758 1
< 0.1%
10011253 1
< 0.1%
10012922 1
< 0.1%
10013339 1
< 0.1%
10015214 1
< 0.1%
10015607 1
< 0.1%
10020793 1
< 0.1%
10021105 1
< 0.1%
ValueCountFrequency (%)
99995104 1
< 0.1%
99994709 1
< 0.1%
99994128 1
< 0.1%
99992769 1
< 0.1%
99991075 1
< 0.1%
99989379 1
< 0.1%
99987336 1
< 0.1%
99981238 1
< 0.1%
99981060 1
< 0.1%
99980366 1
< 0.1%

Gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
Male
26552 
Female
21523 
Other
 
457

Length

Max length6
Median length4
Mean length4.8963776
Min length4

Characters and Unicode

Total characters237631
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 26552
54.7%
Female 21523
44.3%
Other 457
 
0.9%

Length

2025-03-26T21:21:45.060853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-26T21:21:45.135217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 26552
54.7%
female 21523
44.3%
other 457
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e 70055
29.5%
a 48075
20.2%
l 48075
20.2%
M 26552
 
11.2%
F 21523
 
9.1%
m 21523
 
9.1%
O 457
 
0.2%
t 457
 
0.2%
h 457
 
0.2%
r 457
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 237631
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 70055
29.5%
a 48075
20.2%
l 48075
20.2%
M 26552
 
11.2%
F 21523
 
9.1%
m 21523
 
9.1%
O 457
 
0.2%
t 457
 
0.2%
h 457
 
0.2%
r 457
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 237631
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 70055
29.5%
a 48075
20.2%
l 48075
20.2%
M 26552
 
11.2%
F 21523
 
9.1%
m 21523
 
9.1%
O 457
 
0.2%
t 457
 
0.2%
h 457
 
0.2%
r 457
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 237631
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 70055
29.5%
a 48075
20.2%
l 48075
20.2%
M 26552
 
11.2%
F 21523
 
9.1%
m 21523
 
9.1%
O 457
 
0.2%
t 457
 
0.2%
h 457
 
0.2%
r 457
 
0.2%

State/UT
Categorical

Distinct36
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
Gujarat
9184 
Maharashtra
5076 
Uttar Pradesh
4627 
Rajasthan
3803 
Telangana
2799 
Other values (31)
23043 

Length

Max length40
Median length17
Mean length10.010797
Min length3

Characters and Unicode

Total characters485844
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAndhra Pradesh
2nd rowMadhya Pradesh
3rd rowManipur
4th rowRajasthan
5th rowUttar Pradesh

Common Values

ValueCountFrequency (%)
Gujarat 9184
18.9%
Maharashtra 5076
 
10.5%
Uttar Pradesh 4627
 
9.5%
Rajasthan 3803
 
7.8%
Telangana 2799
 
5.8%
Andhra Pradesh 2784
 
5.7%
Karnataka 2723
 
5.6%
Madhya Pradesh 1790
 
3.7%
Tamil Nadu 1754
 
3.6%
Punjab 1005
 
2.1%
Other values (26) 12987
26.8%

Length

2025-03-26T21:21:45.229494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pradesh 10168
15.5%
gujarat 9184
14.0%
maharashtra 5076
 
7.7%
uttar 4627
 
7.0%
rajasthan 3803
 
5.8%
telangana 2799
 
4.3%
andhra 2784
 
4.2%
karnataka 2723
 
4.1%
and 2032
 
3.1%
madhya 1790
 
2.7%
Other values (37) 20774
31.6%

Most occurring characters

ValueCountFrequency (%)
a 122047
25.1%
r 48701
 
10.0%
h 38593
 
7.9%
t 33939
 
7.0%
n 23698
 
4.9%
s 23603
 
4.9%
d 23523
 
4.8%
17228
 
3.5%
e 17097
 
3.5%
u 14809
 
3.0%
Other values (33) 122606
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 485844
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 122047
25.1%
r 48701
 
10.0%
h 38593
 
7.9%
t 33939
 
7.0%
n 23698
 
4.9%
s 23603
 
4.9%
d 23523
 
4.8%
17228
 
3.5%
e 17097
 
3.5%
u 14809
 
3.0%
Other values (33) 122606
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 485844
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 122047
25.1%
r 48701
 
10.0%
h 38593
 
7.9%
t 33939
 
7.0%
n 23698
 
4.9%
s 23603
 
4.9%
d 23523
 
4.8%
17228
 
3.5%
e 17097
 
3.5%
u 14809
 
3.0%
Other values (33) 122606
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 485844
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 122047
25.1%
r 48701
 
10.0%
h 38593
 
7.9%
t 33939
 
7.0%
n 23698
 
4.9%
s 23603
 
4.9%
d 23523
 
4.8%
17228
 
3.5%
e 17097
 
3.5%
u 14809
 
3.0%
Other values (33) 122606
25.2%
Distinct733
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
2025-03-26T21:21:45.503424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length22
Mean length8.4523613
Min length3

Characters and Unicode

Total characters410210
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowSrikakulam
2nd rowMandla
3rd rowImphal West
4th rowJaipur
5th rowHardoi
ValueCountFrequency (%)
north 842
 
1.5%
south 782
 
1.4%
west 551
 
1.0%
east 504
 
0.9%
sikkim 465
 
0.8%
junagadh 465
 
0.8%
jamnagar 463
 
0.8%
kheda 462
 
0.8%
mumbai 460
 
0.8%
kachchh 453
 
0.8%
Other values (736) 51000
90.4%
2025-03-26T21:21:45.819937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 83298
20.3%
r 33470
 
8.2%
h 25457
 
6.2%
i 24597
 
6.0%
n 23805
 
5.8%
u 19486
 
4.8%
d 15906
 
3.9%
o 15058
 
3.7%
l 14540
 
3.5%
g 12308
 
3.0%
Other values (44) 142285
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 410210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 83298
20.3%
r 33470
 
8.2%
h 25457
 
6.2%
i 24597
 
6.0%
n 23805
 
5.8%
u 19486
 
4.8%
d 15906
 
3.9%
o 15058
 
3.7%
l 14540
 
3.5%
g 12308
 
3.0%
Other values (44) 142285
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 410210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 83298
20.3%
r 33470
 
8.2%
h 25457
 
6.2%
i 24597
 
6.0%
n 23805
 
5.8%
u 19486
 
4.8%
d 15906
 
3.9%
o 15058
 
3.7%
l 14540
 
3.5%
g 12308
 
3.0%
Other values (44) 142285
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 410210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 83298
20.3%
r 33470
 
8.2%
h 25457
 
6.2%
i 24597
 
6.0%
n 23805
 
5.8%
u 19486
 
4.8%
d 15906
 
3.9%
o 15058
 
3.7%
l 14540
 
3.5%
g 12308
 
3.0%
Other values (44) 142285
34.7%

RWA/Residential
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
Residential
39042 
RWA
9490 

Length

Max length11
Median length11
Mean length9.4356713
Min length3

Characters and Unicode

Total characters457932
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResidential
2nd rowResidential
3rd rowResidential
4th rowResidential
5th rowResidential

Common Values

ValueCountFrequency (%)
Residential 39042
80.4%
RWA 9490
 
19.6%

Length

2025-03-26T21:21:45.920060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-26T21:21:46.071018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
residential 39042
80.4%
rwa 9490
 
19.6%

Most occurring characters

ValueCountFrequency (%)
e 78084
17.1%
i 78084
17.1%
R 48532
10.6%
s 39042
8.5%
d 39042
8.5%
n 39042
8.5%
t 39042
8.5%
a 39042
8.5%
l 39042
8.5%
W 9490
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 457932
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 78084
17.1%
i 78084
17.1%
R 48532
10.6%
s 39042
8.5%
d 39042
8.5%
n 39042
8.5%
t 39042
8.5%
a 39042
8.5%
l 39042
8.5%
W 9490
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 457932
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 78084
17.1%
i 78084
17.1%
R 48532
10.6%
s 39042
8.5%
d 39042
8.5%
n 39042
8.5%
t 39042
8.5%
a 39042
8.5%
l 39042
8.5%
W 9490
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 457932
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 78084
17.1%
i 78084
17.1%
R 48532
10.6%
s 39042
8.5%
d 39042
8.5%
n 39042
8.5%
t 39042
8.5%
a 39042
8.5%
l 39042
8.5%
W 9490
 
2.1%
Distinct56
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.5 MiB
2025-03-26T21:21:46.232074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length69
Median length58
Mean length47.67778
Min length10

Characters and Unicode

Total characters2313898
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAndhra Pradesh Eastern Power Distribution Company Limited
2nd rowMadhya Pradesh Madhya Kshetra Vidyut Vitaran Company Limited
3rd rowManipur State Power Distribution Company Limited (MSPDCL)
4th rowAjmer Vidyut Vitran Nigam Ltd
5th rowLucknow Electricity Supply Administration (LESA), Lucknow City
ValueCountFrequency (%)
limited 32471
 
11.0%
company 20447
 
6.9%
power 15036
 
5.1%
corporation 11895
 
4.0%
distribution 10349
 
3.5%
electricity 8892
 
3.0%
of 8679
 
2.9%
vidyut 7707
 
2.6%
vij 7106
 
2.4%
gujarat 7106
 
2.4%
Other values (125) 166576
56.2%
2025-03-26T21:21:46.523276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
247732
 
10.7%
i 196736
 
8.5%
a 183802
 
7.9%
t 152459
 
6.6%
r 141834
 
6.1%
o 123520
 
5.3%
n 106522
 
4.6%
e 105264
 
4.5%
d 84691
 
3.7%
m 79111
 
3.4%
Other values (41) 892227
38.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2313898
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
247732
 
10.7%
i 196736
 
8.5%
a 183802
 
7.9%
t 152459
 
6.6%
r 141834
 
6.1%
o 123520
 
5.3%
n 106522
 
4.6%
e 105264
 
4.5%
d 84691
 
3.7%
m 79111
 
3.4%
Other values (41) 892227
38.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2313898
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
247732
 
10.7%
i 196736
 
8.5%
a 183802
 
7.9%
t 152459
 
6.6%
r 141834
 
6.1%
o 123520
 
5.3%
n 106522
 
4.6%
e 105264
 
4.5%
d 84691
 
3.7%
m 79111
 
3.4%
Other values (41) 892227
38.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2313898
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
247732
 
10.7%
i 196736
 
8.5%
a 183802
 
7.9%
t 152459
 
6.6%
r 141834
 
6.1%
o 123520
 
5.3%
n 106522
 
4.6%
e 105264
 
4.5%
d 84691
 
3.7%
m 79111
 
3.4%
Other values (41) 892227
38.6%

Acceptance Status
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Accepted
40510 
Rejected
8022 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters388256
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAccepted
2nd rowAccepted
3rd rowAccepted
4th rowAccepted
5th rowAccepted

Common Values

ValueCountFrequency (%)
Accepted 40510
83.5%
Rejected 8022
 
16.5%

Length

2025-03-26T21:21:46.617538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-26T21:21:46.664420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
accepted 40510
83.5%
rejected 8022
 
16.5%

Most occurring characters

ValueCountFrequency (%)
e 105086
27.1%
c 89042
22.9%
d 48532
12.5%
t 48532
12.5%
p 40510
 
10.4%
A 40510
 
10.4%
R 8022
 
2.1%
j 8022
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 388256
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 105086
27.1%
c 89042
22.9%
d 48532
12.5%
t 48532
12.5%
p 40510
 
10.4%
A 40510
 
10.4%
R 8022
 
2.1%
j 8022
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 388256
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 105086
27.1%
c 89042
22.9%
d 48532
12.5%
t 48532
12.5%
p 40510
 
10.4%
A 40510
 
10.4%
R 8022
 
2.1%
j 8022
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 388256
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 105086
27.1%
c 89042
22.9%
d 48532
12.5%
t 48532
12.5%
p 40510
 
10.4%
A 40510
 
10.4%
R 8022
 
2.1%
j 8022
 
2.1%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
3 - 4 KW
34065 
4 - 5 KW
7086 
5 - 6 KW
3560 
2 - 3 KW
 
2660
Above 6 KW
 
982

Length

Max length10
Median length8
Mean length8.0404681
Min length8

Characters and Unicode

Total characters390220
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3 - 4 KW
2nd row4 - 5 KW
3rd row3 - 4 KW
4th row3 - 4 KW
5th row3 - 4 KW

Common Values

ValueCountFrequency (%)
3 - 4 KW 34065
70.2%
4 - 5 KW 7086
 
14.6%
5 - 6 KW 3560
 
7.3%
2 - 3 KW 2660
 
5.5%
Above 6 KW 982
 
2.0%
1 - 2 KW 179
 
0.4%

Length

2025-03-26T21:21:46.738965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-26T21:21:46.818697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
kw 48532
25.1%
47550
24.6%
4 41151
21.3%
3 36725
19.0%
5 10646
 
5.5%
6 4542
 
2.4%
2 2839
 
1.5%
above 982
 
0.5%
1 179
 
0.1%

Most occurring characters

ValueCountFrequency (%)
144614
37.1%
W 48532
 
12.4%
K 48532
 
12.4%
- 47550
 
12.2%
4 41151
 
10.5%
3 36725
 
9.4%
5 10646
 
2.7%
6 4542
 
1.2%
2 2839
 
0.7%
A 982
 
0.3%
Other values (5) 4107
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 390220
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
144614
37.1%
W 48532
 
12.4%
K 48532
 
12.4%
- 47550
 
12.2%
4 41151
 
10.5%
3 36725
 
9.4%
5 10646
 
2.7%
6 4542
 
1.2%
2 2839
 
0.7%
A 982
 
0.3%
Other values (5) 4107
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 390220
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
144614
37.1%
W 48532
 
12.4%
K 48532
 
12.4%
- 47550
 
12.2%
4 41151
 
10.5%
3 36725
 
9.4%
5 10646
 
2.7%
6 4542
 
1.2%
2 2839
 
0.7%
A 982
 
0.3%
Other values (5) 4107
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 390220
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
144614
37.1%
W 48532
 
12.4%
K 48532
 
12.4%
- 47550
 
12.2%
4 41151
 
10.5%
3 36725
 
9.4%
5 10646
 
2.7%
6 4542
 
1.2%
2 2839
 
0.7%
A 982
 
0.3%
Other values (5) 4107
 
1.1%
Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
SkyPower Solar
3888 
BrightSun Power
3547 
GreenSpark Solar
3531 
RadiantSun Energy
3365 
SunWave Energy
3117 
Other values (15)
31084 

Length

Max length27
Median length23
Mean length18.890773
Min length13

Characters and Unicode

Total characters916807
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSolarCrest Enterprises
2nd rowSolarHarvest Energy
3rd rowSkyPower Solar
4th rowSunRise Renewable Solutions
5th rowSolarPeak Innovations

Common Values

ValueCountFrequency (%)
SkyPower Solar 3888
 
8.0%
BrightSun Power 3547
 
7.3%
GreenSpark Solar 3531
 
7.3%
RadiantSun Energy 3365
 
6.9%
SunWave Energy 3117
 
6.4%
SunTech Solar Solutions 3028
 
6.2%
SunRise Renewable Solutions 2778
 
5.7%
SolarHarvest Energy 2682
 
5.5%
SolarPeak Innovations 2590
 
5.3%
InfiniteLight Solar 2492
 
5.1%
Other values (10) 17514
36.1%

Length

2025-03-26T21:21:46.912918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
solar 16022
 
15.1%
energy 10597
 
10.0%
solutions 7239
 
6.8%
power 5337
 
5.0%
systems 4886
 
4.6%
technologies 4433
 
4.2%
skypower 3888
 
3.7%
enterprises 3731
 
3.5%
brightsun 3547
 
3.3%
greenspark 3531
 
3.3%
Other values (19) 42934
40.4%

Most occurring characters

ValueCountFrequency (%)
e 93036
 
10.1%
n 80033
 
8.7%
r 77975
 
8.5%
o 71915
 
7.8%
S 69099
 
7.5%
57613
 
6.3%
a 55619
 
6.1%
l 46828
 
5.1%
t 39148
 
4.3%
s 39028
 
4.3%
Other values (26) 286513
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 916807
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 93036
 
10.1%
n 80033
 
8.7%
r 77975
 
8.5%
o 71915
 
7.8%
S 69099
 
7.5%
57613
 
6.3%
a 55619
 
6.1%
l 46828
 
5.1%
t 39148
 
4.3%
s 39028
 
4.3%
Other values (26) 286513
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 916807
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 93036
 
10.1%
n 80033
 
8.7%
r 77975
 
8.5%
o 71915
 
7.8%
S 69099
 
7.5%
57613
 
6.3%
a 55619
 
6.1%
l 46828
 
5.1%
t 39148
 
4.3%
s 39028
 
4.3%
Other values (26) 286513
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 916807
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 93036
 
10.1%
n 80033
 
8.7%
r 77975
 
8.5%
o 71915
 
7.8%
S 69099
 
7.5%
57613
 
6.3%
a 55619
 
6.1%
l 46828
 
5.1%
t 39148
 
4.3%
s 39028
 
4.3%
Other values (26) 286513
31.3%
Distinct366
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size379.3 KiB
Minimum2024-01-01 00:00:00
Maximum2024-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-26T21:21:47.022844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-26T21:21:47.349587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct339
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
2025-03-26T21:21:47.680456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.662058
Min length7

Characters and Unicode

Total characters468919
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)< 0.1%

Sample

1st row2024-11-30
2nd row2024-05-21
3rd row2024-12-07
4th row2024-12-16
5th row2024-08-18
ValueCountFrequency (%)
declined 8022
 
16.5%
2024-12-03 347
 
0.7%
2024-12-13 346
 
0.7%
2024-12-09 341
 
0.7%
2024-12-08 338
 
0.7%
2024-12-16 333
 
0.7%
2024-12-12 332
 
0.7%
2024-12-14 332
 
0.7%
2024-12-11 331
 
0.7%
2024-12-10 328
 
0.7%
Other values (329) 37482
77.2%
2025-03-26T21:21:47.958805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 107955
23.0%
- 80782
17.2%
0 79219
16.9%
1 48588
10.4%
4 45378
9.7%
e 16163
 
3.4%
9 8779
 
1.9%
n 8260
 
1.8%
i 8141
 
1.7%
d 8141
 
1.7%
Other values (10) 57513
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 468919
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 107955
23.0%
- 80782
17.2%
0 79219
16.9%
1 48588
10.4%
4 45378
9.7%
e 16163
 
3.4%
9 8779
 
1.9%
n 8260
 
1.8%
i 8141
 
1.7%
d 8141
 
1.7%
Other values (10) 57513
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 468919
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 107955
23.0%
- 80782
17.2%
0 79219
16.9%
1 48588
10.4%
4 45378
9.7%
e 16163
 
3.4%
9 8779
 
1.9%
n 8260
 
1.8%
i 8141
 
1.7%
d 8141
 
1.7%
Other values (10) 57513
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 468919
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 107955
23.0%
- 80782
17.2%
0 79219
16.9%
1 48588
10.4%
4 45378
9.7%
e 16163
 
3.4%
9 8779
 
1.9%
n 8260
 
1.8%
i 8141
 
1.7%
d 8141
 
1.7%
Other values (10) 57513
12.3%
Distinct274
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
2025-03-26T21:21:48.131673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.5419517
Min length7

Characters and Unicode

Total characters463090
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st row2024-12-18
2nd row2024-09-04
3rd row2024-12-23
4th row2024-12-29
5th row2024-09-15
ValueCountFrequency (%)
declined 8022
 
16.5%
pending 2062
 
4.2%
2024-12-26 763
 
1.6%
2024-12-29 752
 
1.5%
2024-12-25 701
 
1.4%
2024-12-23 700
 
1.4%
2024-12-28 685
 
1.4%
2024-12-30 684
 
1.4%
2024-12-27 679
 
1.4%
2024-12-21 648
 
1.3%
Other values (264) 32836
67.7%
2025-03-26T21:21:48.381266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 112998
24.4%
- 76896
16.6%
0 62796
13.6%
1 57931
12.5%
4 42173
 
9.1%
e 18106
 
3.9%
n 12146
 
2.6%
i 10084
 
2.2%
d 10084
 
2.2%
l 8022
 
1.7%
Other values (10) 51854
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 463090
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 112998
24.4%
- 76896
16.6%
0 62796
13.6%
1 57931
12.5%
4 42173
 
9.1%
e 18106
 
3.9%
n 12146
 
2.6%
i 10084
 
2.2%
d 10084
 
2.2%
l 8022
 
1.7%
Other values (10) 51854
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 463090
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 112998
24.4%
- 76896
16.6%
0 62796
13.6%
1 57931
12.5%
4 42173
 
9.1%
e 18106
 
3.9%
n 12146
 
2.6%
i 10084
 
2.2%
d 10084
 
2.2%
l 8022
 
1.7%
Other values (10) 51854
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 463090
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 112998
24.4%
- 76896
16.6%
0 62796
13.6%
1 57931
12.5%
4 42173
 
9.1%
e 18106
 
3.9%
n 12146
 
2.6%
i 10084
 
2.2%
d 10084
 
2.2%
l 8022
 
1.7%
Other values (10) 51854
11.2%
Distinct201
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
2025-03-26T21:21:48.568140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.2450548
Min length7

Characters and Unicode

Total characters448681
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)< 0.1%

Sample

1st row2024-12-29
2nd row2024-09-13
3rd row2024-12-31
4th rowPending
5th row2024-10-15
ValueCountFrequency (%)
declined 8022
 
16.5%
pending 6865
 
14.1%
2024-12-30 1553
 
3.2%
2024-12-29 1383
 
2.8%
2024-12-28 1283
 
2.6%
2024-12-27 1276
 
2.6%
2024-12-26 1167
 
2.4%
2024-12-31 1149
 
2.4%
2024-12-25 1076
 
2.2%
2024-12-24 1072
 
2.2%
Other values (191) 23686
48.8%
2025-03-26T21:21:48.867031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 109201
24.3%
- 67290
15.0%
1 52011
11.6%
0 46793
10.4%
4 36648
 
8.2%
e 22909
 
5.1%
n 21752
 
4.8%
i 14887
 
3.3%
d 14887
 
3.3%
l 8022
 
1.8%
Other values (10) 54281
12.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 448681
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 109201
24.3%
- 67290
15.0%
1 52011
11.6%
0 46793
10.4%
4 36648
 
8.2%
e 22909
 
5.1%
n 21752
 
4.8%
i 14887
 
3.3%
d 14887
 
3.3%
l 8022
 
1.8%
Other values (10) 54281
12.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 448681
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 109201
24.3%
- 67290
15.0%
1 52011
11.6%
0 46793
10.4%
4 36648
 
8.2%
e 22909
 
5.1%
n 21752
 
4.8%
i 14887
 
3.3%
d 14887
 
3.3%
l 8022
 
1.8%
Other values (10) 54281
12.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 448681
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 109201
24.3%
- 67290
15.0%
1 52011
11.6%
0 46793
10.4%
4 36648
 
8.2%
e 22909
 
5.1%
n 21752
 
4.8%
i 14887
 
3.3%
d 14887
 
3.3%
l 8022
 
1.8%
Other values (10) 54281
12.1%
Distinct120
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
2025-03-26T21:21:49.055532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length8
Mean length8.2583656
Min length7

Characters and Unicode

Total characters400795
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowPending
2nd row2024-12-03
3rd rowPending
4th rowPending
5th row2024-12-16
ValueCountFrequency (%)
pending 22827
47.0%
declined 8022
 
16.5%
2024-12-30 1474
 
3.0%
2024-12-29 1224
 
2.5%
2024-12-31 1173
 
2.4%
2024-12-28 1094
 
2.3%
2024-12-27 990
 
2.0%
2024-12-26 904
 
1.9%
2024-12-25 768
 
1.6%
2024-12-24 759
 
1.6%
Other values (110) 9297
19.2%
2025-03-26T21:21:49.318311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 61279
15.3%
n 53676
13.4%
e 38871
9.7%
- 35366
8.8%
i 30849
7.7%
d 30849
7.7%
1 25468
6.4%
P 22827
 
5.7%
g 22827
 
5.7%
0 22420
 
5.6%
Other values (10) 56363
14.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 400795
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 61279
15.3%
n 53676
13.4%
e 38871
9.7%
- 35366
8.8%
i 30849
7.7%
d 30849
7.7%
1 25468
6.4%
P 22827
 
5.7%
g 22827
 
5.7%
0 22420
 
5.6%
Other values (10) 56363
14.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 400795
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 61279
15.3%
n 53676
13.4%
e 38871
9.7%
- 35366
8.8%
i 30849
7.7%
d 30849
7.7%
1 25468
6.4%
P 22827
 
5.7%
g 22827
 
5.7%
0 22420
 
5.6%
Other values (10) 56363
14.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 400795
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 61279
15.3%
n 53676
13.4%
e 38871
9.7%
- 35366
8.8%
i 30849
7.7%
d 30849
7.7%
1 25468
6.4%
P 22827
 
5.7%
g 22827
 
5.7%
0 22420
 
5.6%
Other values (10) 56363
14.1%
Distinct66
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2025-03-26T21:21:49.445647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length7
Mean length7.5126927
Min length7

Characters and Unicode

Total characters364606
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowPending
2nd row2024-12-21
3rd rowPending
4th rowPending
5th row2024-12-31
ValueCountFrequency (%)
pending 34890
71.9%
declined 8022
 
16.5%
2024-12-30 711
 
1.5%
2024-12-31 586
 
1.2%
2024-12-29 536
 
1.1%
2024-12-28 479
 
1.0%
2024-12-27 397
 
0.8%
2024-12-26 362
 
0.7%
2024-12-25 286
 
0.6%
2024-12-24 266
 
0.5%
Other values (56) 1997
 
4.1%
2025-03-26T21:21:49.650272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 77802
21.3%
e 50934
14.0%
d 42912
11.8%
i 42912
11.8%
P 34890
9.6%
g 34890
9.6%
2 20159
 
5.5%
- 11240
 
3.1%
c 8022
 
2.2%
l 8022
 
2.2%
Other values (10) 32823
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 364606
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 77802
21.3%
e 50934
14.0%
d 42912
11.8%
i 42912
11.8%
P 34890
9.6%
g 34890
9.6%
2 20159
 
5.5%
- 11240
 
3.1%
c 8022
 
2.2%
l 8022
 
2.2%
Other values (10) 32823
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 364606
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 77802
21.3%
e 50934
14.0%
d 42912
11.8%
i 42912
11.8%
P 34890
9.6%
g 34890
9.6%
2 20159
 
5.5%
- 11240
 
3.1%
c 8022
 
2.2%
l 8022
 
2.2%
Other values (10) 32823
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 364606
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 77802
21.3%
e 50934
14.0%
d 42912
11.8%
i 42912
11.8%
P 34890
9.6%
g 34890
9.6%
2 20159
 
5.5%
- 11240
 
3.1%
c 8022
 
2.2%
l 8022
 
2.2%
Other values (10) 32823
9.0%

Subsidy Redeemed Date
Categorical

High correlation  Imbalance 

Distinct32
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Pending
39636 
Declined
8022 
2024-12-30
 
174
2024-12-31
 
133
2024-12-29
 
119
Other values (27)
 
448

Length

Max length10
Median length7
Mean length7.2193192
Min length7

Characters and Unicode

Total characters350368
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowPending
2nd rowPending
3rd rowPending
4th rowPending
5th rowPending

Common Values

ValueCountFrequency (%)
Pending 39636
81.7%
Declined 8022
 
16.5%
2024-12-30 174
 
0.4%
2024-12-31 133
 
0.3%
2024-12-29 119
 
0.2%
2024-12-28 86
 
0.2%
2024-12-27 60
 
0.1%
2024-12-25 51
 
0.1%
2024-12-26 51
 
0.1%
2024-12-22 34
 
0.1%
Other values (22) 166
 
0.3%

Length

2025-03-26T21:21:49.728875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pending 39636
81.7%
declined 8022
 
16.5%
2024-12-30 174
 
0.4%
2024-12-31 133
 
0.3%
2024-12-29 119
 
0.2%
2024-12-28 86
 
0.2%
2024-12-27 60
 
0.1%
2024-12-25 51
 
0.1%
2024-12-26 51
 
0.1%
2024-12-22 34
 
0.1%
Other values (22) 166
 
0.3%

Most occurring characters

ValueCountFrequency (%)
n 87294
24.9%
e 55680
15.9%
d 47658
13.6%
i 47658
13.6%
P 39636
11.3%
g 39636
11.3%
D 8022
 
2.3%
c 8022
 
2.3%
l 8022
 
2.3%
2 3150
 
0.9%
Other values (10) 5590
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 350368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 87294
24.9%
e 55680
15.9%
d 47658
13.6%
i 47658
13.6%
P 39636
11.3%
g 39636
11.3%
D 8022
 
2.3%
c 8022
 
2.3%
l 8022
 
2.3%
2 3150
 
0.9%
Other values (10) 5590
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 350368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 87294
24.9%
e 55680
15.9%
d 47658
13.6%
i 47658
13.6%
P 39636
11.3%
g 39636
11.3%
D 8022
 
2.3%
c 8022
 
2.3%
l 8022
 
2.3%
2 3150
 
0.9%
Other values (10) 5590
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 350368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 87294
24.9%
e 55680
15.9%
d 47658
13.6%
i 47658
13.6%
P 39636
11.3%
g 39636
11.3%
D 8022
 
2.3%
c 8022
 
2.3%
l 8022
 
2.3%
2 3150
 
0.9%
Other values (10) 5590
 
1.6%

Subsidy Released Date
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Pending
40507 
Declined
8022 
2024-12-31
 
2
2024-12-25
 
1

Length

Max length10
Median length7
Mean length7.1654784
Min length7

Characters and Unicode

Total characters347755
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPending
2nd rowPending
3rd rowPending
4th rowPending
5th rowPending

Common Values

ValueCountFrequency (%)
Pending 40507
83.5%
Declined 8022
 
16.5%
2024-12-31 2
 
< 0.1%
2024-12-25 1
 
< 0.1%

Length

2025-03-26T21:21:49.818253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-26T21:21:49.899824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
pending 40507
83.5%
declined 8022
 
16.5%
2024-12-31 2
 
< 0.1%
2024-12-25 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 89036
25.6%
e 56551
16.3%
d 48529
14.0%
i 48529
14.0%
P 40507
11.6%
g 40507
11.6%
D 8022
 
2.3%
c 8022
 
2.3%
l 8022
 
2.3%
2 10
 
< 0.1%
Other values (6) 20
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 347755
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 89036
25.6%
e 56551
16.3%
d 48529
14.0%
i 48529
14.0%
P 40507
11.6%
g 40507
11.6%
D 8022
 
2.3%
c 8022
 
2.3%
l 8022
 
2.3%
2 10
 
< 0.1%
Other values (6) 20
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 347755
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 89036
25.6%
e 56551
16.3%
d 48529
14.0%
i 48529
14.0%
P 40507
11.6%
g 40507
11.6%
D 8022
 
2.3%
c 8022
 
2.3%
l 8022
 
2.3%
2 10
 
< 0.1%
Other values (6) 20
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 347755
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 89036
25.6%
e 56551
16.3%
d 48529
14.0%
i 48529
14.0%
P 40507
11.6%
g 40507
11.6%
D 8022
 
2.3%
c 8022
 
2.3%
l 8022
 
2.3%
2 10
 
< 0.1%
Other values (6) 20
 
< 0.1%
Distinct181
Distinct (%)0.4%
Missing8022
Missing (%)16.5%
Memory size2.2 MiB
2025-03-26T21:21:50.135117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.1675636
Min length1

Characters and Unicode

Total characters87808
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row28
2nd row104
3rd row17
4th row38
5th row83
ValueCountFrequency (%)
83 389
 
1.0%
74 388
 
1.0%
81 382
 
0.9%
73 378
 
0.9%
75 378
 
0.9%
77 376
 
0.9%
86 374
 
0.9%
76 370
 
0.9%
84 370
 
0.9%
72 369
 
0.9%
Other values (171) 36736
90.7%
2025-03-26T21:21:50.464903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 17545
20.0%
2 9000
10.2%
3 8264
9.4%
7 7732
8.8%
4 7718
8.8%
5 7614
8.7%
6 7586
8.6%
8 7536
8.6%
9 7252
8.3%
0 6728
 
7.7%
Other values (6) 833
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87808
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 17545
20.0%
2 9000
10.2%
3 8264
9.4%
7 7732
8.8%
4 7718
8.8%
5 7614
8.7%
6 7586
8.6%
8 7536
8.6%
9 7252
8.3%
0 6728
 
7.7%
Other values (6) 833
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87808
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 17545
20.0%
2 9000
10.2%
3 8264
9.4%
7 7732
8.8%
4 7718
8.8%
5 7614
8.7%
6 7586
8.6%
8 7536
8.6%
9 7252
8.3%
0 6728
 
7.7%
Other values (6) 833
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87808
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 17545
20.0%
2 9000
10.2%
3 8264
9.4%
7 7732
8.8%
4 7718
8.8%
5 7614
8.7%
6 7586
8.6%
8 7536
8.6%
9 7252
8.3%
0 6728
 
7.7%
Other values (6) 833
 
0.9%
Distinct172
Distinct (%)0.4%
Missing8022
Missing (%)16.5%
Memory size2.2 MiB
2025-03-26T21:21:50.731863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.3237966
Min length1

Characters and Unicode

Total characters94137
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row19
2nd row107
3rd row17
4th row14
5th row29
ValueCountFrequency (%)
pending 2062
 
5.1%
9 735
 
1.8%
12 733
 
1.8%
13 729
 
1.8%
11 721
 
1.8%
10 716
 
1.8%
14 700
 
1.7%
17 696
 
1.7%
16 671
 
1.7%
15 654
 
1.6%
Other values (162) 32093
79.2%
2025-03-26T21:21:51.108452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 15865
16.9%
2 10291
10.9%
3 8948
9.5%
4 7849
8.3%
5 7044
7.5%
6 6338
 
6.7%
7 6149
 
6.5%
8 5928
 
6.3%
9 5921
 
6.3%
0 5370
 
5.7%
Other values (6) 14434
15.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 94137
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 15865
16.9%
2 10291
10.9%
3 8948
9.5%
4 7849
8.3%
5 7044
7.5%
6 6338
 
6.7%
7 6149
 
6.5%
8 5928
 
6.3%
9 5921
 
6.3%
0 5370
 
5.7%
Other values (6) 14434
15.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 94137
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 15865
16.9%
2 10291
10.9%
3 8948
9.5%
4 7849
8.3%
5 7044
7.5%
6 6338
 
6.7%
7 6149
 
6.5%
8 5928
 
6.3%
9 5921
 
6.3%
0 5370
 
5.7%
Other values (6) 14434
15.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 94137
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 15865
16.9%
2 10291
10.9%
3 8948
9.5%
4 7849
8.3%
5 7044
7.5%
6 6338
 
6.7%
7 6149
 
6.5%
8 5928
 
6.3%
9 5921
 
6.3%
0 5370
 
5.7%
Other values (6) 14434
15.3%
Distinct158
Distinct (%)0.4%
Missing8022
Missing (%)16.5%
Memory size2.2 MiB
2025-03-26T21:21:51.317573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.8042952
Min length1

Characters and Unicode

Total characters113602
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st row12
2nd row10
3rd row9
4th rowPending
5th row31
ValueCountFrequency (%)
pending 6865
 
16.9%
9 1561
 
3.9%
10 1407
 
3.5%
11 1283
 
3.2%
12 1242
 
3.1%
13 1205
 
3.0%
8 1107
 
2.7%
14 1083
 
2.7%
15 1054
 
2.6%
16 1007
 
2.5%
Other values (148) 22696
56.0%
2025-03-26T21:21:51.626139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 15805
13.9%
n 13730
12.1%
2 10204
 
9.0%
3 7575
 
6.7%
d 6865
 
6.0%
i 6865
 
6.0%
e 6865
 
6.0%
P 6865
 
6.0%
g 6865
 
6.0%
4 6017
 
5.3%
Other values (6) 25946
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113602
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 15805
13.9%
n 13730
12.1%
2 10204
 
9.0%
3 7575
 
6.7%
d 6865
 
6.0%
i 6865
 
6.0%
e 6865
 
6.0%
P 6865
 
6.0%
g 6865
 
6.0%
4 6017
 
5.3%
Other values (6) 25946
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113602
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 15805
13.9%
n 13730
12.1%
2 10204
 
9.0%
3 7575
 
6.7%
d 6865
 
6.0%
i 6865
 
6.0%
e 6865
 
6.0%
P 6865
 
6.0%
g 6865
 
6.0%
4 6017
 
5.3%
Other values (6) 25946
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113602
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 15805
13.9%
n 13730
12.1%
2 10204
 
9.0%
3 7575
 
6.7%
d 6865
 
6.0%
i 6865
 
6.0%
e 6865
 
6.0%
P 6865
 
6.0%
g 6865
 
6.0%
4 6017
 
5.3%
Other values (6) 25946
22.8%
Distinct120
Distinct (%)0.3%
Missing8022
Missing (%)16.5%
Memory size2.3 MiB
2025-03-26T21:21:51.802484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length4.8196742
Min length2

Characters and Unicode

Total characters195245
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowPending
2nd row82
3rd rowPending
4th rowPending
5th row63
ValueCountFrequency (%)
pending 22827
56.3%
17 1492
 
3.7%
18 1213
 
3.0%
16 1144
 
2.8%
19 1127
 
2.8%
20 987
 
2.4%
21 895
 
2.2%
22 790
 
2.0%
23 687
 
1.7%
24 679
 
1.7%
Other values (110) 8669
 
21.4%
2025-03-26T21:21:52.049079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 45654
23.4%
P 22827
11.7%
e 22827
11.7%
d 22827
11.7%
i 22827
11.7%
g 22827
11.7%
2 8134
 
4.2%
1 6775
 
3.5%
3 4228
 
2.2%
7 2765
 
1.4%
Other values (6) 13554
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 195245
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 45654
23.4%
P 22827
11.7%
e 22827
11.7%
d 22827
11.7%
i 22827
11.7%
g 22827
11.7%
2 8134
 
4.2%
1 6775
 
3.5%
3 4228
 
2.2%
7 2765
 
1.4%
Other values (6) 13554
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 195245
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 45654
23.4%
P 22827
11.7%
e 22827
11.7%
d 22827
11.7%
i 22827
11.7%
g 22827
11.7%
2 8134
 
4.2%
1 6775
 
3.5%
3 4228
 
2.2%
7 2765
 
1.4%
Other values (6) 13554
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 195245
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 45654
23.4%
P 22827
11.7%
e 22827
11.7%
d 22827
11.7%
i 22827
11.7%
g 22827
11.7%
2 8134
 
4.2%
1 6775
 
3.5%
3 4228
 
2.2%
7 2765
 
1.4%
Other values (6) 13554
 
6.9%
Distinct70
Distinct (%)0.2%
Missing8022
Missing (%)16.5%
Memory size2.4 MiB
2025-03-26T21:21:52.158929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.3063935
Min length2

Characters and Unicode

Total characters255472
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowPending
2nd row19
3rd rowPending
4th rowPending
5th row16
ValueCountFrequency (%)
pending 34890
86.1%
17 690
 
1.7%
18 587
 
1.4%
16 566
 
1.4%
19 481
 
1.2%
20 360
 
0.9%
21 335
 
0.8%
22 290
 
0.7%
23 288
 
0.7%
24 232
 
0.6%
Other values (60) 1791
 
4.4%
2025-03-26T21:21:52.364597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 69780
27.3%
P 34890
13.7%
e 34890
13.7%
d 34890
13.7%
i 34890
13.7%
g 34890
13.7%
1 2792
 
1.1%
2 2708
 
1.1%
3 1044
 
0.4%
7 931
 
0.4%
Other values (6) 3767
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 255472
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 69780
27.3%
P 34890
13.7%
e 34890
13.7%
d 34890
13.7%
i 34890
13.7%
g 34890
13.7%
1 2792
 
1.1%
2 2708
 
1.1%
3 1044
 
0.4%
7 931
 
0.4%
Other values (6) 3767
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 255472
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 69780
27.3%
P 34890
13.7%
e 34890
13.7%
d 34890
13.7%
i 34890
13.7%
g 34890
13.7%
1 2792
 
1.1%
2 2708
 
1.1%
3 1044
 
0.4%
7 931
 
0.4%
Other values (6) 3767
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 255472
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 69780
27.3%
P 34890
13.7%
e 34890
13.7%
d 34890
13.7%
i 34890
13.7%
g 34890
13.7%
1 2792
 
1.1%
2 2708
 
1.1%
3 1044
 
0.4%
7 931
 
0.4%
Other values (6) 3767
 
1.5%

Inspection to Subsidy Redeemed Days
Categorical

High correlation  Imbalance  Missing 

Distinct34
Distinct (%)0.1%
Missing8022
Missing (%)16.5%
Memory size2.6 MiB
Pending
39636 
17
 
151
16
 
138
18
 
121
19
 
91
Other values (29)
 
373

Length

Max length7
Median length7
Mean length6.8921254
Min length2

Characters and Unicode

Total characters279200
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowPending
2nd rowPending
3rd rowPending
4th rowPending
5th rowPending

Common Values

ValueCountFrequency (%)
Pending 39636
81.7%
17 151
 
0.3%
16 138
 
0.3%
18 121
 
0.2%
19 91
 
0.2%
20 77
 
0.2%
21 56
 
0.1%
23 41
 
0.1%
22 33
 
0.1%
24 23
 
< 0.1%
Other values (24) 143
 
0.3%
(Missing) 8022
 
16.5%

Length

2025-03-26T21:21:52.443231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pending 39636
97.8%
17 151
 
0.4%
16 138
 
0.3%
18 121
 
0.3%
19 91
 
0.2%
20 77
 
0.2%
21 56
 
0.1%
23 41
 
0.1%
22 33
 
0.1%
24 23
 
0.1%
Other values (24) 143
 
0.4%

Most occurring characters

ValueCountFrequency (%)
n 79272
28.4%
P 39636
14.2%
e 39636
14.2%
d 39636
14.2%
i 39636
14.2%
g 39636
14.2%
1 567
 
0.2%
2 344
 
0.1%
7 174
 
0.1%
6 161
 
0.1%
Other values (6) 502
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 279200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 79272
28.4%
P 39636
14.2%
e 39636
14.2%
d 39636
14.2%
i 39636
14.2%
g 39636
14.2%
1 567
 
0.2%
2 344
 
0.1%
7 174
 
0.1%
6 161
 
0.1%
Other values (6) 502
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 279200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 79272
28.4%
P 39636
14.2%
e 39636
14.2%
d 39636
14.2%
i 39636
14.2%
g 39636
14.2%
1 567
 
0.2%
2 344
 
0.1%
7 174
 
0.1%
6 161
 
0.1%
Other values (6) 502
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 279200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 79272
28.4%
P 39636
14.2%
e 39636
14.2%
d 39636
14.2%
i 39636
14.2%
g 39636
14.2%
1 567
 
0.2%
2 344
 
0.1%
7 174
 
0.1%
6 161
 
0.1%
Other values (6) 502
 
0.2%

Subsidy Redeemed to Released Days
Categorical

High correlation  Imbalance  Missing 

Distinct4
Distinct (%)< 0.1%
Missing8022
Missing (%)16.5%
Memory size2.6 MiB
Pending
40507 
32
 
1
34
 
1
31
 
1

Length

Max length7
Median length7
Mean length6.9996297
Min length2

Characters and Unicode

Total characters283555
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowPending
2nd rowPending
3rd rowPending
4th rowPending
5th rowPending

Common Values

ValueCountFrequency (%)
Pending 40507
83.5%
32 1
 
< 0.1%
34 1
 
< 0.1%
31 1
 
< 0.1%
(Missing) 8022
 
16.5%

Length

2025-03-26T21:21:52.529897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-26T21:21:52.660839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
pending 40507
> 99.9%
32 1
 
< 0.1%
34 1
 
< 0.1%
31 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 81014
28.6%
P 40507
14.3%
e 40507
14.3%
d 40507
14.3%
i 40507
14.3%
g 40507
14.3%
3 3
 
< 0.1%
2 1
 
< 0.1%
4 1
 
< 0.1%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 283555
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 81014
28.6%
P 40507
14.3%
e 40507
14.3%
d 40507
14.3%
i 40507
14.3%
g 40507
14.3%
3 3
 
< 0.1%
2 1
 
< 0.1%
4 1
 
< 0.1%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 283555
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 81014
28.6%
P 40507
14.3%
e 40507
14.3%
d 40507
14.3%
i 40507
14.3%
g 40507
14.3%
3 3
 
< 0.1%
2 1
 
< 0.1%
4 1
 
< 0.1%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 283555
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 81014
28.6%
P 40507
14.3%
e 40507
14.3%
d 40507
14.3%
i 40507
14.3%
g 40507
14.3%
3 3
 
< 0.1%
2 1
 
< 0.1%
4 1
 
< 0.1%
1 1
 
< 0.1%

Interactions

2025-03-26T21:21:43.653881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-26T21:21:52.723855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Acceptance StatusApplication NumberGenderInspection to Subsidy Redeemed DaysProduction Capacity (KW)RWA/ResidentialState/UTSubsidy Redeemed DateSubsidy Redeemed to Released DaysSubsidy Released DateVendor Organization
Acceptance Status1.0000.0000.0001.0000.0080.0000.1731.0001.0001.0000.009
Application Number0.0001.0000.0090.0040.0040.0000.0040.0020.0000.0000.000
Gender0.0000.0091.0000.0080.0020.0000.0100.0000.0000.0000.007
Inspection to Subsidy Redeemed Days1.0000.0040.0081.0000.0220.0000.0140.4100.4550.5290.009
Production Capacity (KW)0.0080.0040.0020.0221.0000.0070.0060.0170.0000.0000.000
RWA/Residential0.0000.0000.0000.0000.0071.0000.0760.0000.0000.0000.005
State/UT0.1730.0040.0100.0140.0060.0761.0000.0310.0000.0980.000
Subsidy Redeemed Date1.0000.0020.0000.4100.0170.0000.0311.0001.0001.0000.010
Subsidy Redeemed to Released Days1.0000.0000.0000.4550.0000.0000.0001.0001.0001.0000.007
Subsidy Released Date1.0000.0000.0000.5290.0000.0000.0981.0001.0001.0000.006
Vendor Organization0.0090.0000.0070.0090.0000.0050.0000.0100.0070.0061.000

Missing values

2025-03-26T21:21:43.852848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-26T21:21:44.137098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-26T21:21:44.533352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Application NumberGenderState/UTDistrictRWA/ResidentialDiscom NameAcceptance StatusProduction Capacity (KW)Vendor OrganizationRegistration DateApplication Approved DateVendor Selection DateVendor Acceptance DateInstallation DateInspection DateSubsidy Redeemed DateSubsidy Released DateRegistration to Approval DaysApproval to Vendor Selection DaysVendor Selection to Acceptance DaysAcceptance to Installation DaysInstallation to Inspection DaysInspection to Subsidy Redeemed DaysSubsidy Redeemed to Released Days
067744287FemaleAndhra PradeshSrikakulamResidentialAndhra Pradesh Eastern Power Distribution Company LimitedAccepted3 - 4 KWSolarCrest Enterprises2024-11-032024-11-302024-12-182024-12-29PendingPendingPendingPending281912PendingPendingPendingPending
158136760FemaleMadhya PradeshMandlaResidentialMadhya Pradesh Madhya Kshetra Vidyut Vitaran Company LimitedAccepted4 - 5 KWSolarHarvest Energy2024-02-082024-05-212024-09-042024-09-132024-12-032024-12-21PendingPending104107108219PendingPending
268329500MaleManipurImphal WestResidentialManipur State Power Distribution Company Limited (MSPDCL)Accepted3 - 4 KWSkyPower Solar2024-11-212024-12-072024-12-232024-12-31PendingPendingPendingPending17179PendingPendingPendingPending
316851319FemaleRajasthanJaipurResidentialAjmer Vidyut Vitran Nigam LtdAccepted3 - 4 KWSunRise Renewable Solutions2024-11-092024-12-162024-12-29PendingPendingPendingPendingPending3814PendingPendingPendingPendingPending
473638420MaleUttar PradeshHardoiResidentialLucknow Electricity Supply Administration (LESA), Lucknow CityAccepted3 - 4 KWSolarPeak Innovations2024-05-282024-08-182024-09-152024-10-152024-12-162024-12-31PendingPending8329316316PendingPending
578115114MaleGujaratPanchmahalRWATorrent Power Limited, AhmedabadAccepted3 - 4 KWSunRise Renewable Solutions2024-03-142024-05-252024-09-192024-11-172024-12-152024-12-30PendingPending73118602916PendingPending
622070304FemaleGujaratDevbhoomi DwarkaResidentialUttar Gujarat Vij Company Limited (UGVCL), MehsanaAccepted3 - 4 KWSunTech Solar Solutions2024-07-142024-09-042024-10-272024-11-172024-12-18PendingPendingPending53542232PendingPendingPending
716505513FemaleUttar PradeshDeoriaResidentialMadhyanchal Vidyut Vitaran Nigam Limited (MVVNL), Lucknow ZoneAccepted5 - 6 KWGreenEnergy Systems2024-05-132024-06-302024-10-302024-12-132024-12-28PendingPendingPending491234516PendingPendingPending
859804207MaleGujaratChhota UdaipurResidentialPaschim Gujarat Vij Company Limited (PGVCL), RajkotAccepted3 - 4 KWPowerSun Technologies2024-05-112024-08-282024-11-102024-12-19PendingPendingPendingPending1107540PendingPendingPendingPending
992171419MaleUttar PradeshPratapgarhResidentialKanpur Electricity Supply Company (KESCO), Kanpur CityAccepted3 - 4 KWSolarHarvest Energy2024-05-042024-07-102024-11-062024-12-22PendingPendingPendingPending6812047PendingPendingPendingPending
Application NumberGenderState/UTDistrictRWA/ResidentialDiscom NameAcceptance StatusProduction Capacity (KW)Vendor OrganizationRegistration DateApplication Approved DateVendor Selection DateVendor Acceptance DateInstallation DateInspection DateSubsidy Redeemed DateSubsidy Released DateRegistration to Approval DaysApproval to Vendor Selection DaysVendor Selection to Acceptance DaysAcceptance to Installation DaysInstallation to Inspection DaysInspection to Subsidy Redeemed DaysSubsidy Redeemed to Released Days
4852227191786FemaleGujaratMorbiRWAUttar Gujarat Vij Company Limited (UGVCL), MehsanaAccepted3 - 4 KWSkyPower Solar2024-12-252024-12-30PendingPendingPendingPendingPendingPending6PendingPendingPendingPendingPendingPending
4852390834756MaleGujaratMahisagarRWAPaschim Gujarat Vij Company Limited (PGVCL), RajkotAccepted3 - 4 KWIlluminateSun Technologies2024-10-282024-12-132024-12-27PendingPendingPendingPendingPending4715PendingPendingPendingPendingPending
4852411110345MaleUttar PradeshMathuraResidentialLucknow Electricity Supply Administration (LESA), Lucknow CityAccepted3 - 4 KWSunWave Energy2024-07-152024-10-072024-11-132024-12-162024-12-31PendingPendingPending85383416PendingPendingPending
4852571241787FemaleUttar PradeshMoradabadResidentialDakshinanchal Vidyut Vitaran Nigam Limited (DVVNL), Agra ZoneAccepted4 - 5 KWSolarCrest Enterprises2024-07-152024-09-282024-11-052024-12-032024-12-23PendingPendingPending76392921PendingPendingPending
4852690896974FemaleChandigarhChandigarhResidentialPowerGrid Corporation of IndiaAccepted5 - 6 KWInfiniteLight Solar2024-12-042024-12-27PendingPendingPendingPendingPendingPending24PendingPendingPendingPendingPendingPending
4852758366975MaleUttar PradeshMaharajganjResidentialDakshinanchal Vidyut Vitaran Nigam Limited (DVVNL), Agra ZoneAccepted3 - 4 KWIlluminateSun Technologies2024-11-252024-12-162024-12-25PendingPendingPendingPendingPending2210PendingPendingPendingPendingPending
4852881835906MaleRajasthanJhalawarResidentialAjmer Vidyut Vitran Nigam LtdAccepted3 - 4 KWRadiantSun Energy2024-04-152024-07-122024-12-112024-12-22PendingPendingPendingPending8915312PendingPendingPendingPending
4852985159780MaleBiharSupaulResidentialSouth Bihar Power Distribution Company Limited (SBPDCL)Accepted4 - 5 KWSolarPeak Innovations2024-03-052024-07-302024-10-142024-12-072024-12-26PendingPendingPending148775520PendingPendingPending
4853066177024FemaleJharkhandHazaribaghResidentialPowerGrid Corporation of IndiaRejected3 - 4 KWSolarPeak Innovations2024-08-12DeclinedDeclinedDeclinedDeclinedDeclinedDeclinedDeclinedNaNNaNNaNNaNNaNNaNNaN
4853120235135FemalePunjabJalandharResidentialPunjab State Power Corporation Limited (PSPCL)Accepted3 - 4 KWGreenSpark Solar2024-05-082024-08-202024-12-122024-12-24PendingPendingPendingPending10511513PendingPendingPendingPending